Successful consumer loyalty programs hinge on having reliable, accurate, and trusted data.
Large amounts of inaccurate contact data make loyalty programs less effective and not cost effective to the organizations. One major root cause of “bad” data is duplicate records. Conflicting data is corruptive to the integrity of databases.
In Qivos after years of expertise, we assure databases we are handling are cleaned and trusted. So, how we clean up databases from duplicates?
In a recent project for a big client with several brands we implemented the following solution to clear up their loyalty program’s databases:
To detect inaccurate data, IT department was involved applying database queries. Records were exported to excel for visual check by the agent: name, cell, home, address, city, birthday date (day, month) and preferred store.
As duplicates records we defined:
a. Loyalty members having same mobile number and same name in various brand loyalty schemes,
b. Loyalty members without or invalid mobile number and same name in various brand loyalty schemes,
c. Loyalty members without contact number and same name / address in various brand loyalty schemes.
We ended up to the following cleansing actions:
• Loyalty member’s inactive account transactions are merged to active account.
• If both accounts are active then all transactions are merged to the account with the most recent transaction.
• If both accounts are inactive then all transactions are merged to the account with biggest spent amount.
• If both accounts are inactive and spent amount is the same then all transactions are merged to the account with the most recent creation date.
Don’t let inaccurate data harm your marketing efforts!